Workshop
Fri Jul 28 12:00 PM -- 08:00 PM (PDT) @ Meeting Room 323 None
Structured Probabilistic Inference and Generative Modeling
The workshop focuses on theory, methodology, and application of structured probabilistic inference and generative modeling, both of which are important topics in machine learning.Specifically, probabilistic inference addresses the problem of amortization,sampling, and integration of complex quantities from graphical models, while generative modeling captures the underlying probability distributions of a dataset. Apart from applications in computer vision, natural language processing, and speech recognition, probabilistic inference and generative modeling approaches have also been widely used in natural science domains, including physics, chemistry, molecular biology, and medicine. Despite the promising results, probabilistic methods face challenges when applied to highly structured data, which are ubiquitous in real-world settings, limiting the applications of such methods. This workshop aims to bring experts from diverse backgrounds and related domains together to discuss the applications and challenges of probabilistic methods. The workshop will emphasize challenges in encoding domain knowledge when learning representations, performing inference and generations. By bringing together experts from academia and industry, the workshop will provide a platform for researchers to share their latest results and ideas, fostering collaboration and discussion in the field of probabilistic methods.
| Opening Remark | |
| Invited Talk by Karen Ullrich (Invited Talk) | |
| Invited Talk by Tommi Jaakkola (Invited Talk) | |
| Coffee Break (Break) | |
| Invited Talk by Durk Kingma (Invited Talk) | |
| Collapsed Inference for Bayesian Deep Learning (Contributed Talk) | |
| Provable benefits of score matching (Contributed Talk) | |
| Poster Session 1 (Poster Session) | |
| Panel Discussion | |
| Invited Talk by Ruqi Zhang (Invited Talk) | |
| Invited Talk by Stefano Ermon (Invited Talk) | |
| BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery (Contributed Talk) | |
| Generative Marginalization Models (Contributed Talk) | |
| Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network (Contributed Talk) | |
| Closing Remark | |
| Poster Session 2 (Poster Session) | |
| Inferring Hierarchical Structure in Multi-Room Maze Environments (Poster) | |
| AbODE: Ab initio antibody design using conjoined ODEs (Poster) | |
| Reinforcement Learning-Driven Linker Design via Fast Attention-based Point Cloud Alignment (Poster) | |
| Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes (Poster) | |
| Score-based Enhanced Sampling for Protein Molecular Dynamics (Poster) | |
| Large Dimensional Change Point Detection with FWER Control as Automatic Stopping (Poster) | |
| Diffusion Probabilistic Models for Structured Node Classification (Poster) | |
| DiffMol: 3D Structured Molecule Generation with Discrete Denoising Diffusion Probabilistic Models (Poster) | |
| Dimensionality Reduction as Probabilistic Inference (Poster) | |
| PRODIGY: Enabling In-context Learning Over Graphs (Poster) | |
| Balanced Training of Energy-Based Models with Adaptive Flow Sampling (Poster) | |
| BatchGFN: Generative Flow Networks for Batch Active Learning (Poster) | |
| Lexinvariant Language Models (Poster) | |
| Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Markov Chains (Poster) | |
| Improving Training of Likelihood-based Generative Models with Gaussian Homotopy (Poster) | |
| Uncovering Latent Structure Using Random Partition Models (Poster) | |
| On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization (Poster) | |
| BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery (Oral) | |
| Variational Point Encoding Deformation for Dental Modeling (Poster) | |
| Autoregressive Diffusion Models with non-Uniform Generation Order (Poster) | |
| Neuro-Causal Factor Analysis (Poster) | |
| Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies (Poster) | |
| STable Permutation-based Framework for Table Generation in Sequence-to-Sequence Models (Poster) | |
| Morse Neural Networks for Uncertainty Quantification (Poster) | |
| Tree Variational Autoencoders (Poster) | |
| Diffusion Generative Inverse Design (Poster) | |
| Structured Neural Networks for Density Estimation (Poster) | |
| Fast and Functional structured data generator (Poster) | |
| HiGen: Hierarchical Graph Generative Networks (Poster) | |
| Causal Discovery with Language Models as Imperfect Experts (Poster) | |
| Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection (Poster) | |
| Learning Linear Causal Representations from Interventions under General Nonlinear Mixing (Poster) | |
| Flow Matching for Scalable Simulation-Based Inference (Poster) | |
| CM-GAN: Stabilizing GAN Training with Consistency Models (Poster) | |
| Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data (Poster) | |
| MissDiff: Training Diffusion Models on Tabular Data with Missing Values (Poster) | |
| Early Exiting for Accelerated Inference in Diffusion Models (Poster) | |
| Generative Marginalization Models (Oral) | |
| Identifying Under-Reported Events in Networks with Spatial Latent Variable Models (Poster) | |
| Beyond Intuition, a Framework for Applying GPs to Real-World Data (Poster) | |
| Function Space Bayesian Pseudocoreset for Bayesian Neural Networks (Poster) | |
| Exploring Exchangeable Dataset Amortization for Bayesian Posterior Inference (Poster) | |
| Collaborative Score Distillation for Consistent Visual Synthesis (Poster) | |
| Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation (Poster) | |
| Generating Turn-Based Player Behavior via Experience from Demonstrations (Poster) | |
| Training Diffusion Models with Reinforcement Learning (Poster) | |
| Nested Diffusion Processes for Anytime Image Generation (Poster) | |
| An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction (Poster) | |
| Provable benefits of score matching (Oral) | |
| Diffusion Based Causal Representation Learning (Poster) | |
| Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network (Oral) | |
| Prediction under Latent Subgroup Shifts with High-dimensional Observations (Poster) | |
| Attention as Implicit Structural Inference (Poster) | |
| Solving Inverse Physics Problems with Score Matching (Poster) | |
| Diffusion Probabilistic Models Generalize when They Fail to Memorize (Poster) | |
| Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models (Poster) | |
| Nonparametric posterior normalizing flows (Poster) | |
| An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets (Poster) | |
| C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder (Poster) | |
| Diffusion map particle systems for generative modeling (Poster) | |
| Scaling Graphically Structured Diffusion Models (Poster) | |
| Implications of kernel mismatch for OOD data (Poster) | |
| Beyond Confidence: Reliable Models Should Also Consider Atypicality (Poster) | |
| Test-time Adaptation with Diffusion Models (Poster) | |
| Your Diffusion Model is Secretly a Zero-Shot Classifier (Poster) | |
| Collapsed Inference for Bayesian Deep Learning (Oral) | |
| Visual Chain-of-Thought Diffusion Models (Poster) | |
| A Generative Model for Text Control in Minecraft (Poster) | |
| Non-Normal Diffusion Models (Poster) | |
| Pretrained Language Models to Solve Graph Tasks in Natural Language (Poster) | |
| Towards Modular Learning of Deep Causal Generative Models (Poster) | |
| The Local Inconsistency Resolution Algorithm (Poster) | |
| Plug-and-Play Controllable Graph Generation with Diffusion Models (Poster) | |
| The Pairwise Prony Algorithm: Efficient Inference of Stochastic Block Models with Prescribed Subgraph Densities (Poster) | |
| Graph Neural Network Powered Bayesian Optimization for Large Molecular Spaces (Poster) | |
| Deep Generative Clustering with Multimodal Variational Autoencoders (Poster) | |
| Conditional Graph Generation with Graph Principal Flow Network (Poster) | |
| Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation (Poster) | |
| Generative semi-supervised learning with a neural seq2seq noisy channel (Poster) | |
| Practical and Asymptotically Exact Conditional Sampling in Diffusion Models (Poster) | |
| Anomaly Detection in Networks via Score-Based Generative Models (Poster) | |
| HINT: Hierarchical Coherent Networks For Constrained Probabilistic Forecasting (Poster) | |
| Multilevel Control Functional (Poster) | |
| Diffusion Models with Grouped Latents for Interpretable Latent Space (Poster) | |
| Concept Algebra for Score-based Conditional Model (Poster) | |
| GFlowNets for Causal Discovery: an Overview (Poster) | |
| Thompson Sampling for Improved Exploration in GFlowNets (Poster) | |
| Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences (Poster) | |
| Hierarchical Graph Generation with $K^{2}$-trees (Poster) | |
| GSURE-Based Diffusion Model Training with Corrupted Data (Poster) | |
| Robust and Scalable Bayesian Online Changepoint Detection (Poster) | |
| Optimizing protein fitness using Bi-level Gibbs sampling with Graph-based Smoothing (Poster) | |
| On the Identifiability of Markov Switching Models (Poster) | |
| Regularized Data Programming with Automated Bayesian Prior Selection (Poster) | |
| PITS: Variational Pitch Inference Without Fundamental Frequency for End-to-End Pitch-Controllable TTS (Poster) | |
| Parallel Sampling of Diffusion Models (Poster) | |